352 research outputs found
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
Traffic signal control is an emerging application scenario for reinforcement
learning. Besides being as an important problem that affects people's daily
life in commuting, traffic signal control poses its unique challenges for
reinforcement learning in terms of adapting to dynamic traffic environment and
coordinating thousands of agents including vehicles and pedestrians. A key
factor in the success of modern reinforcement learning relies on a good
simulator to generate a large number of data samples for learning. The most
commonly used open-source traffic simulator SUMO is, however, not scalable to
large road network and large traffic flow, which hinders the study of
reinforcement learning on traffic scenarios. This motivates us to create a new
traffic simulator CityFlow with fundamentally optimized data structures and
efficient algorithms. CityFlow can support flexible definitions for road
network and traffic flow based on synthetic and real-world data. It also
provides user-friendly interface for reinforcement learning. Most importantly,
CityFlow is more than twenty times faster than SUMO and is capable of
supporting city-wide traffic simulation with an interactive render for
monitoring. Besides traffic signal control, CityFlow could serve as the base
for other transportation studies and can create new possibilities to test
machine learning methods in the intelligent transportation domain.Comment: WWW 2019 Demo Pape
SR-OOD: Out-of-Distribution Detection via Sample Repairing
It is widely reported that deep generative models can classify
out-of-distribution (OOD) samples as in-distribution with high confidence. In
this work, we propose a hypothesis that this phenomenon is due to the
reconstruction task, which can cause the generative model to focus too much on
low-level features and not enough on semantic information. To address this
issue, we introduce SR-OOD, an OOD detection framework that utilizes sample
repairing to encourage the generative model to learn more than just an identity
map. By focusing on semantics, our framework improves OOD detection performance
without external data and label information. Our experimental results
demonstrate the competitiveness of our approach in detecting OOD samples
Improving Strength and Microstructure of SiC Reticulated Porous Ceramic through In-Situ Generation of SiC Whiskers within Hollow Voids
SiC Reticulated Porous Ceramic with Excellent Strength and High-Density Ceramic Struts Was Successfully Prepared using the Polymer Replica Method, Followed by Pressureless Sintering under a Buried Charcoal Atmosphere. First, a Polyurethane (PU) Template Was Coated with a Si Slurry and Then a SiC-Containing Slurry, and Subsequently Heated under the Buried Charcoal Atmosphere. to Ensure Excellent Coating Ability of the Slurries, the Viscosity, Thixotropy, and Yield Stresses of the Si Slurry Were Optimized by Adjusting the Content of the Thickening Agent. during Heating, Si in the Coating Layer Reacted with the Residual C and CO Gas from the PU Template and Buried Charcoal, Forming SiC Whiskers that Filled Hollow Voids within the SiC Struts. Additionally, Catalyst Ferric Nitrate Was Added to the Si Slurry to Promote the Generation and Growth of SiC Whiskers. as a Result, When Compared to the Untreated SiC Reticulated Porous Ceramic, the SiC Reticulated Porous Ceramic Pre-Coated with Si Layers Exhibited Significant Improvements in Mechanical Strength and Thermal Shock Resistance, Despite Minor Differences in Porosity. Furthermore, an Industrial Test Conducted in the Copper Smelting Industry Showed that the Structure of SiC Reticulated Porous Ceramic, Prepared in This Study and Used as Filters, Remained Intact Even after 7 Days of Continuous Use. Meanwhile, a Significant Number of Inclusions Was Adhered to the Surfaces of the Filters. Therefore, the Processes Combined with In-Situ Generation of SiC Whiskers is an Ideal and Low-Cost Method for Fabricating SiC Filters with Excellent Properties
Discovery of Novel Insulin Sensitizers: Promising Approaches and Targets
Insulin resistance is the undisputed root cause of type 2 diabetes mellitus (T2DM). There is currently an unmet demand for safe and effective insulin sensitizers, owing to the restricted prescription or removal from market of certain approved insulin sensitizers, such as thiazolidinediones (TZDs), because of safety concerns. Effective insulin sensitizers without TZD-like side effects will therefore be invaluable to diabetic patients. The specific focus on peroxisome proliferator-activated receptor Ī³- (PPARĪ³-) based agents in the past decades may have impeded the search for novel and safer insulin sensitizers. This review discusses possible directions and promising strategies for future research and development of novel insulin sensitizers and describes the potential targets of these agents. Direct PPARĪ³ agonists, selective PPARĪ³ modulators (sPPARĪ³Ms), PPARĪ³-sparing compounds (including ligands of the mitochondrial target of TZDs), agents that target the downstream effectors of PPARĪ³, along with agents, such as heat shock protein (HSP) inducers, 5ā²-adenosine monophosphate-activated protein kinase (AMPK) activators, 11Ī²-hydroxysteroid dehydrogenase type 1 (11Ī²-HSD1) selective inhibitors, biguanides, and chloroquines, which may be safer than traditional TZDs, have been described. This minireview thus aims to provide fresh perspectives for the development of a new generation of safe insulin sensitizers
The influence of adatom diffusion on the formation of skyrmion lattice in sub-monolayer Fe on Ir(111)
Room temperature grown Fe monolayer (ML) on the Ir(111) single crystal
substrate has attracted great research interests as nano-skyrmion lattice can
form under proper growth conditions. The formation of the nanoscale skyrmion,
however, appears to be greatly affected by the diffusion length of the Fe
adatoms on the Ir(111) surface. We made this observation by employing
spin-polarized scanning tunneling microscopy to study skyrmion formation upon
systematically changing the impurity density on the substrate surface prior to
Fe deposition. Since the substrate surface impurities serve as pinning centers
for Fe adatoms, the eventual size and shape of the Fe islands exhibit a direct
correlation with the impurity density, which in turn determines whether
skyrmion can be formed. Our observation indicates that skyrmion only forms when
the impurity density is below 0.006/nm2, i.e., 12 nm averaged spacing between
the neighboring defects. We verify the significance of Fe diffusion length by
growing Fe on clean Ir(111) substrate at low temperature of 30 K, where no
skyrmion was observed to form. Our findings signify the importance of diffusion
of Fe atoms on the Ir(111) substrate, which affects the size, shape and lattice
perfection of the Fe islands and thus the formation of skyrmion lattice
Creation of nano-skyrmion lattice in Fe/Ir(111) system using voltage pulse
Magnetic ultrathin films grown on heavy metal substrates often exhibit rich
spin structures due to the competition between various magnetic interactions
such as Heisenberg exchange, Dzyaloshinskii-Moriya interaction and higher-order
spin interactions. Here we employ spin-polarized scanning tunneling microscopy
to study magnetic nano-skyrmion phase in Fe monolayer grown on Ir(111)
substrate. Our observations show that the formation of nano-skyrmion lattice in
the Fe/Ir(111) system depends sensitively on the growth conditions and various
non-skyrmion spin states can be formed. Remarkably, the application of voltage
pulses between the tip and the sample can trigger a non-skyrmion to skyrmion
phase transition. The fact that nano-skyrmions can be created using voltage
pulse indicates that the balance between the competing magnetic interactions
can be affected by an external electric field, which is highly useful to design
skyrmion-based spintronic devices with low energy consumption
Networked Time Series Prediction with Incomplete Data
A networked time series (NETS) is a family of time series on a given graph,
one for each node. It has a wide range of applications from intelligent
transportation, environment monitoring to smart grid management. An important
task in such applications is to predict the future values of a NETS based on
its historical values and the underlying graph. Most existing methods require
complete data for training. However, in real-world scenarios, it is not
uncommon to have missing data due to sensor malfunction, incomplete sensing
coverage, etc. In this paper, we study the problem of NETS prediction with
incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that
can be trained on incomplete data with missing values in both history and
future. Furthermore, we propose Graph Temporal Attention Networks, which
incorporate the attention mechanism to capture both inter-time series and
temporal correlations. We conduct extensive experiments on four real-world
datasets under different missing patterns and missing rates. The experimental
results show that NETS-ImpGAN outperforms existing methods, reducing the MAE by
up to 25%
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